Natural Language Generation (NLG) is a young but growing research field, whose goal is to build computer systems that automatically produce fluent and effective texts in various human languages. Generally, NLG systems have used knowledge databases containing general world knowledge and specific domain knowledge, together with various linguistic resources (e.g., lexicons, grammars, discourse relations), to produce texts with limited variation in word choice, sentence and discourse structure, and virtually no variation in rhetorical style or pragmatic purpose.
While various computational systems have been devised as solutions to the problem of producing documents with limited expressiveness in form and effect, none has presented a general solution to the problem of representing the kinds of knowledge that are needed to produce documents tailored to a specific use or audience in a manner that is systematic and extensible, and that further provides for the authoring of such documents by a non-computer-programmer professional writer. In addition, no system has yet presented a general solution for automatically integrating various aspects of document design (e.g., text, graphics, and presentation layout) into a single consistent representation format for use within a document intended for customization.
It is well-known from studies in communication that presentation of information in a manner that is tailored to the characteristics of a particular audience can be significant both in maintaining the interest of the members of the audience and in effectively conveying the meaning of the information. For example, in the health care industry, it has been shown that information that is tailored to the characteristics of an individual patient can have a far greater effect in producing compliance with suggested medical regimens as compared to generic information. (Stretcher et al 1994 have done pioneering work in this area).
But as Strecher et al's behavioural studies also showed, a sizeable number of different medical and personality factors had to be taken into account in producing customized health information that would have the desired effect on the intended patient. DiMarco et al (1995) noted that this kind of customization involves much more than producing each brochure or leaflet in half a dozen different versions for different audiences. Rather, the number of different combinations could easily be in the tens of thousands. While not all distinct combinations might need distinct customizations, it is nonetheless impossible in general to produce and distribute, in advance of need, the large number of different editions of each publication that is required for individual tailoring of information.
Thus there is a need for a computer system for the automated production of customized material that would tailor a general-purpose “master document” for a particular purpose or individual on demand. It must also be remembered that in the present context the term “document” is broadly used to define any textual or non-textual data, including multimedia and hypertext, having inter-relationships between the data, and that may be displayable or presented to a human audience in one of many presentations and formats.
As a further example, a master document may refer to the complete superset of instructions to direct the actions of a robot on an assembly line. In this instance, there exists a need to tailor or adaptively generate subsets of combinations of instructions for specific robot applications. Whether the master document is to be customized for a particular purpose, as in the robot example, or tailored for a specific audience, as in the case of health information, this process of adaptive document generation should be easily implementable on a computer system at minimum possible cost and maximum possible ease of use to both the author of the master document and the user of the generation system.
In the field of natural processing, or, computational linguistics, various computer systems have been implemented which attempt to produce customized documents. In the simplest cases, simple mail-merge techniques are used which enable “personalized” documents to be generated by using hand-coded decision rules indicating what information is to be included for various tailoring situations. However, these techniques result in very inflexible, and often, awkwardly structured, and poorly cohesive texts. Other systems utilize schema-based techniques to select and organize the content data according to simple document-template structures. But these templates are either too general-purpose to provide anything more than very coarse-grained adaptation in the resulting customized texts or too specific to the application in question to be appropriate for general use in adaptive generation systems.
A number of projects have used more sophisticated techniques from NLG research to build adaptive generation systems for both written texts and hypertext documents. The IDAS project (Reiter, Melish, and Levine 1995) recognized the need to tailor both textual and non-textual information, including visual formatting, hypertext input, and graphics output. IDAS also tried to address the need for explicit authoring tools in the adaptive document generation process, but here the focus was on authoring at the knowledge-base level (i.e., at the level of a computer system's internal representation), while there still exists a need to provide an authoring tool that may be used by a non-computer-programmer professional writer who could compose the master document at the level of ordinary English, with additional markup as required (e.g., HTML markup to support an HTML presentation format for a resulting customized version of the document). IDAS relies mainly on canned texts and aims to provide the user with a means of navigating through the whole “hyperspace” of possible (canned) texts. There is however a need to provide for a much finer-grained degree of tailoring than the IDAS implementation.
While IDAS relies mainly on canned texts, other adaptive generation systems do use more-dynamic text generation: the Migraine system (Carenini, Mittal, and Moore 1994) uses an approach to text planning that adaptively selects and structures the information to be given to a particular reader. However, Migraine relies on a large number of context-sensitive and user-sensitive “text plans” (i.e., text schemas) so that its methods of tailoring must of necessity be very specific to its particular domain. The PEBA-II system (milosavljevic and Dale 1996) uses more-general text plans, as well as text templates, that it can choose from to adapt information to the individual reader, but the tailoring done is very specific, focussing on the user's familiarity with a topic. The PIGLET system (Cawsey, Binsted, and Jones 1995) also uses a combination of text plans and text templates, but its tailoring is also quite specific in nature, mainly concerned with emphasizing material that is relevant to a particular patient. The ILEX-0 system (Knott, Mellish, Oberlander, and O'Donnell 1996) is similar to the PIGLET model in its anticipation of all the possible texts that might be generated, but also includes annotations (e.g., a condition on a piece of canned text) to allow some local customization. However, very free and flexible use of annotations could lead to problems of repetitive text and inappropriate use of referring expressions in the resulting document, requiring textual repair.
None of the previous systems provide for a text-repair facility of the kind described by Hovy and Wanner (1996) and Wanner and Hovy (1996). The paradigm of adaptive document generation by “selection and repair”, as introduced by DiMarco, Hirst, Wilkinson, and Wanner (1995), that is, selection of the relevant pieces of information from a master document, and then repair of any syntactic or stylistic problems in the resulting document by a text-repair facility, is central to the goals of a customizable document system. However, the system should be able to support either an adaptive generation system with full facilities for selecting and repairing texts, as described by DiMarco, Hirst, Wilkinson, and Wanner (1995) and Hirst, DiMarco, Hovy, and Parsons (1997), or a simpler version of the system, based on “generation by selection only”, i.e., with no facilities for textual repair, an implementation of which (called “WebbeDoc”) is described by DiMarco and Foster (1997).
In summary, an author of a customizable document needs to be able to describe the variations of a document, which may be both textual and non-textual, at various levels of the document structure, together with conditions for selecting each variation.
The author then needs a means of selecting all the appropriate variations for a particular purpose or audience, re-assembling the selected variations into a coherent document, and producing an appropriately customized version of the document, in potentially many different levels of representation (e.g., surface English, a deep syntactic or semantic representation for use in textual repair) and presentation formats (e.g. HTML, LaTeX).
None of the existing adaptive document generation systems has provided a generally applicable method and apparatus for describing all the different ways in which a document could be customized, or for providing for a non-computer-programmer author of a customizable document to specify the possible variations, or for selecting the appropriate variations and producing a customized version of a document.